Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China
Infectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on dia...
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Online Access: | http://dx.doi.org/10.1155/2020/8814222 |
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doaj-83a0cdb9875446938caddb1b78fa4a3e2021-07-02T14:50:21ZengHindawi LimitedScientific Programming1058-92441875-919X2020-01-01202010.1155/2020/88142228814222Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, ChinaZhijin Wang0Yaohui Huang1Bingyan He2Ting Luo3Yongming Wang4Yonggang Fu5Computer Engineering College, Jimei University, Xiamen 361021, ChinaChengyi University College, Jimei University, Xiamen 361021, ChinaComputer Engineering College, Jimei University, Xiamen 361021, ChinaChengyi University College, Jimei University, Xiamen 361021, ChinaChina Electronics Technology Group Corporation, Shanghai 200001, ChinaComputer Engineering College, Jimei University, Xiamen 361021, ChinaInfectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on diarrhea data. To address this issue, this paper proposes a parsimonious model (PM), which takes historical outpatient visit counts, meteorological factors (MFs) and Baidu search indices (BSIs) as inputs to perform prediction. An experimental evaluation was done to compare the short-term prediction performance of ten algorithms for four groups of inputs, using data collected in Xiamen, China. Results show that the proposed method is effective in improving the prediction accuracy.http://dx.doi.org/10.1155/2020/8814222 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhijin Wang Yaohui Huang Bingyan He Ting Luo Yongming Wang Yonggang Fu |
spellingShingle |
Zhijin Wang Yaohui Huang Bingyan He Ting Luo Yongming Wang Yonggang Fu Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China Scientific Programming |
author_facet |
Zhijin Wang Yaohui Huang Bingyan He Ting Luo Yongming Wang Yonggang Fu |
author_sort |
Zhijin Wang |
title |
Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China |
title_short |
Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China |
title_full |
Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China |
title_fullStr |
Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China |
title_full_unstemmed |
Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China |
title_sort |
short-term infectious diarrhea prediction using weather and search data in xiamen, china |
publisher |
Hindawi Limited |
series |
Scientific Programming |
issn |
1058-9244 1875-919X |
publishDate |
2020-01-01 |
description |
Infectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on diarrhea data. To address this issue, this paper proposes a parsimonious model (PM), which takes historical outpatient visit counts, meteorological factors (MFs) and Baidu search indices (BSIs) as inputs to perform prediction. An experimental evaluation was done to compare the short-term prediction performance of ten algorithms for four groups of inputs, using data collected in Xiamen, China. Results show that the proposed method is effective in improving the prediction accuracy. |
url |
http://dx.doi.org/10.1155/2020/8814222 |
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